Assumption-free noise suppression for autonomous tractors tracking

L. Marata, J. M. Chuma, A. Yahya, I. Ngebani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Autonomous tractors have gained high interest from researchers due to the need for increased productivity in Agriculture. Their application include ploughing, weeding and crop spraying. One problem of these tractors which has not been fully addressed is tracking using the noisy measurements from a sensor such as RADAR sensor. Most publications assume the error in the measurement to be Gaussian during the position estimation process. This assumption has seen a poor performance of the estimators in case the sensor noise is non-Gaussian. This research work introduces the use of Separable Monte Carlos based Mean for non-Gaussian noise suppression applied to Autonomous tractor tracking. The Monte Carlos based Means work independent from any assumptions. Gaussian and Cauchy Noise are used in experimentation for RADAR sensor measurement. Results suggest that the Separable Monte Carlos based mean (SMC-MEAN) outperforms the Kalman Filter and the Maximum A Posterior (MAP) in the Mean square error (MSE) sense hence can be of practical use in Autonomous tractor tracking.

Original languageEnglish
Title of host publication2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509025800
DOIs
Publication statusPublished - Dec 5 2016
Event2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016 - Saint-Gilles Les Bains, Réunion
Duration: Oct 10 2016Oct 13 2016

Other

Other2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016
CountryRéunion
CitySaint-Gilles Les Bains
Period10/10/1610/13/16

Fingerprint

tractors
retarding
sensors
Sensors
plowing
agriculture
crops
spraying
experimentation
Kalman filters
Spraying
random noise
productivity
estimators
Mean square error
Agriculture
Crops
Productivity

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Instrumentation

Cite this

Marata, L., Chuma, J. M., Yahya, A., & Ngebani, I. (2016). Assumption-free noise suppression for autonomous tractors tracking. In 2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016 [7772035] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RADIO.2016.7772035
Marata, L. ; Chuma, J. M. ; Yahya, A. ; Ngebani, I. / Assumption-free noise suppression for autonomous tractors tracking. 2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016. Institute of Electrical and Electronics Engineers Inc., 2016.
@inproceedings{f47689e09145483a837bdd9f418bffc5,
title = "Assumption-free noise suppression for autonomous tractors tracking",
abstract = "Autonomous tractors have gained high interest from researchers due to the need for increased productivity in Agriculture. Their application include ploughing, weeding and crop spraying. One problem of these tractors which has not been fully addressed is tracking using the noisy measurements from a sensor such as RADAR sensor. Most publications assume the error in the measurement to be Gaussian during the position estimation process. This assumption has seen a poor performance of the estimators in case the sensor noise is non-Gaussian. This research work introduces the use of Separable Monte Carlos based Mean for non-Gaussian noise suppression applied to Autonomous tractor tracking. The Monte Carlos based Means work independent from any assumptions. Gaussian and Cauchy Noise are used in experimentation for RADAR sensor measurement. Results suggest that the Separable Monte Carlos based mean (SMC-MEAN) outperforms the Kalman Filter and the Maximum A Posterior (MAP) in the Mean square error (MSE) sense hence can be of practical use in Autonomous tractor tracking.",
author = "L. Marata and Chuma, {J. M.} and A. Yahya and I. Ngebani",
year = "2016",
month = "12",
day = "5",
doi = "10.1109/RADIO.2016.7772035",
language = "English",
booktitle = "2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Marata, L, Chuma, JM, Yahya, A & Ngebani, I 2016, Assumption-free noise suppression for autonomous tractors tracking. in 2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016., 7772035, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016, Saint-Gilles Les Bains, Réunion, 10/10/16. https://doi.org/10.1109/RADIO.2016.7772035

Assumption-free noise suppression for autonomous tractors tracking. / Marata, L.; Chuma, J. M.; Yahya, A.; Ngebani, I.

2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7772035.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Assumption-free noise suppression for autonomous tractors tracking

AU - Marata, L.

AU - Chuma, J. M.

AU - Yahya, A.

AU - Ngebani, I.

PY - 2016/12/5

Y1 - 2016/12/5

N2 - Autonomous tractors have gained high interest from researchers due to the need for increased productivity in Agriculture. Their application include ploughing, weeding and crop spraying. One problem of these tractors which has not been fully addressed is tracking using the noisy measurements from a sensor such as RADAR sensor. Most publications assume the error in the measurement to be Gaussian during the position estimation process. This assumption has seen a poor performance of the estimators in case the sensor noise is non-Gaussian. This research work introduces the use of Separable Monte Carlos based Mean for non-Gaussian noise suppression applied to Autonomous tractor tracking. The Monte Carlos based Means work independent from any assumptions. Gaussian and Cauchy Noise are used in experimentation for RADAR sensor measurement. Results suggest that the Separable Monte Carlos based mean (SMC-MEAN) outperforms the Kalman Filter and the Maximum A Posterior (MAP) in the Mean square error (MSE) sense hence can be of practical use in Autonomous tractor tracking.

AB - Autonomous tractors have gained high interest from researchers due to the need for increased productivity in Agriculture. Their application include ploughing, weeding and crop spraying. One problem of these tractors which has not been fully addressed is tracking using the noisy measurements from a sensor such as RADAR sensor. Most publications assume the error in the measurement to be Gaussian during the position estimation process. This assumption has seen a poor performance of the estimators in case the sensor noise is non-Gaussian. This research work introduces the use of Separable Monte Carlos based Mean for non-Gaussian noise suppression applied to Autonomous tractor tracking. The Monte Carlos based Means work independent from any assumptions. Gaussian and Cauchy Noise are used in experimentation for RADAR sensor measurement. Results suggest that the Separable Monte Carlos based mean (SMC-MEAN) outperforms the Kalman Filter and the Maximum A Posterior (MAP) in the Mean square error (MSE) sense hence can be of practical use in Autonomous tractor tracking.

UR - http://www.scopus.com/inward/record.url?scp=85010468140&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85010468140&partnerID=8YFLogxK

U2 - 10.1109/RADIO.2016.7772035

DO - 10.1109/RADIO.2016.7772035

M3 - Conference contribution

AN - SCOPUS:85010468140

BT - 2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Marata L, Chuma JM, Yahya A, Ngebani I. Assumption-free noise suppression for autonomous tractors tracking. In 2016 IEEE Radio and Antenna Days of the Indian Ocean, RADIO 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7772035 https://doi.org/10.1109/RADIO.2016.7772035